Výuka geoinformatických předmětů na příkladech dat Evropské Unie

Zdeňka Dobešová, Karel Macků, Michal Kučera
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Abstract

Data Mining and Advanced Geodata Processing are compulsory courses for the Master's degree in Geoinformatics and Cartography at Palacký University. The practical exercises use data provided by European authorities. In both courses, these are data provided by Eurostat, the statistical office of the European Union, and data provided by the European Environment Agency under Copernicus Land Monitoring Service - Urban Atlas. The benefit of the practical exercises is not only to practise different methods of analysis but also to become familiar with the sources of European Union data. Thus, the practical examples increase the knowledge of European geographical topics and the possibilities of obtaining data from freely available sources. In the Data Mining course, the topics of correlation, principal component analysis, hierarchical and non-hierarchical clustering are practiced on employment data according to the NACE Level1 economic activity code. Time series analysis is practiced on rail traffic data in EU countries. Of interest is the identification of passenger and freight traffic trends from 2005 to 2021 in each European country. Furthermore, the quarterly changes in traffic due to the covid-19 pandemic in 2020 and 2021 can be well identified from the data. Similarity search procedures are shown on Urban Atlas data. In addition, the use of trained neural networks is practiced to find the similarity of European cities according to land use centres of cities. The semester assignment is also based on European data. In the exercise, the data mining software Orange is used with a visual programming language. The Advanced Geodata Processing course focuses primarily on topics related to spatial statistics - first, it guides students through advanced exploratory analysis methods, then spatially weighted methods are introduced, followed by the use of spatial regression models. The second part of the syllabus consists of the use of geocomputation methods, in which students are introduced to the topics of fuzzy logic, information theory and fractal geometry and their applications in space. Regional NUTS2 statistics from Eurostat and OECD database are used for the exercises. Educational texts such as the Orange software textbook and exercise book for self-practice on EU data are freely available, including source data and program codes, on the project website http://urbandm.upol.cz/ in the Teaching Materials section.
数据挖掘和高级地理数据处理是Palacký大学地理信息与制图专业硕士学位的必修课。实际演练使用了欧洲当局提供的数据。在这两门课程中,这些数据都是由欧盟统计局提供的,以及由欧洲环境署在哥白尼土地监测服务-城市地图集下提供的数据。实践练习的好处不仅在于练习不同的分析方法,还在于熟悉欧盟数据的来源。因此,实际例子增加了对欧洲地理专题的认识,并增加了从免费来源获得数据的可能性。在数据挖掘课程中,根据NACE一级经济活动代码,对就业数据进行相关性、主成分分析、层次聚类和非层次聚类等主题的实践。对欧盟国家的轨道交通数据进行了时间序列分析。有趣的是,从2005年到2021年,每个欧洲国家的客运和货运交通趋势的确定。此外,从数据中可以很好地识别出2020年和2021年covid-19大流行导致的季度流量变化。相似搜索程序显示在城市地图集数据上。此外,利用训练有素的神经网络,根据城市的土地利用中心来寻找欧洲城市的相似性。学期作业也是基于欧洲的数据。在练习中,数据挖掘软件Orange与一种可视化编程语言一起使用。高级地理数据处理课程主要侧重于与空间统计相关的主题-首先,它引导学生通过先进的探索性分析方法,然后介绍空间加权方法,然后使用空间回归模型。教学大纲的第二部分包括地理计算方法的应用,向学生介绍模糊逻辑、信息论和分形几何及其在空间中的应用。这些活动使用了来自欧盟统计局和经合发组织数据库的区域NUTS2统计数据。在项目网站http://urbandm.upol.cz/的教材部分,可以免费获得Orange软件教科书和用于欧盟数据自我练习的练习本等教育文本,包括源数据和程序代码。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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